Overview

Dataset statistics

Number of variables28
Number of observations21957
Missing cells83888
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.5 MiB
Average record size in memory884.5 B

Variable types

Numeric15
Text5
Categorical4
DateTime4

Alerts

10 km Miejsce Open is highly overall correlated with 10 km Tempo and 10 other fieldsHigh correlation
10 km Tempo is highly overall correlated with 10 km Miejsce Open and 9 other fieldsHigh correlation
15 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
15 km Tempo is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
20 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
20 km Tempo is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
5 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
5 km Tempo is highly overall correlated with 10 km Miejsce Open and 9 other fieldsHigh correlation
Kategoria wiekowa is highly overall correlated with Płeć and 1 other fieldsHigh correlation
Kategoria wiekowa Miejsce is highly overall correlated with 15 km Miejsce Open and 6 other fieldsHigh correlation
Miejsce is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
Numer startowy is highly overall correlated with 10 km Miejsce Open and 2 other fieldsHigh correlation
Płeć is highly overall correlated with Kategoria wiekowaHigh correlation
Płeć Miejsce is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
Rocznik is highly overall correlated with Kategoria wiekowaHigh correlation
Tempo is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
Tempo Stabilność is highly overall correlated with 15 km Tempo and 1 other fieldsHigh correlation
Kraj is highly imbalanced (95.5%)Imbalance
Miejsce has 3507 (16.0%) missing valuesMissing
Miasto has 4183 (19.1%) missing valuesMissing
Kraj has 3507 (16.0%) missing valuesMissing
Drużyna has 13555 (61.7%) missing valuesMissing
Płeć Miejsce has 3507 (16.0%) missing valuesMissing
Kategoria wiekowa Miejsce has 3527 (16.1%) missing valuesMissing
Rocznik has 485 (2.2%) missing valuesMissing
5 km Czas has 3546 (16.1%) missing valuesMissing
5 km Miejsce Open has 3546 (16.1%) missing valuesMissing
5 km Tempo has 3546 (16.1%) missing valuesMissing
10 km Czas has 3530 (16.1%) missing valuesMissing
10 km Miejsce Open has 3530 (16.1%) missing valuesMissing
10 km Tempo has 3562 (16.2%) missing valuesMissing
15 km Czas has 3529 (16.1%) missing valuesMissing
15 km Miejsce Open has 3529 (16.1%) missing valuesMissing
15 km Tempo has 3544 (16.1%) missing valuesMissing
20 km Czas has 3518 (16.0%) missing valuesMissing
20 km Miejsce Open has 3518 (16.0%) missing valuesMissing
20 km Tempo has 3535 (16.1%) missing valuesMissing
Tempo Stabilność has 3580 (16.3%) missing valuesMissing
Czas has 2055 (9.4%) missing valuesMissing
Tempo has 3507 (16.0%) missing valuesMissing
Rocznik is highly skewed (γ1 = -25.54009856)Skewed

Reproduction

Analysis started2025-05-05 18:45:40.938110
Analysis finished2025-05-05 18:45:54.237904
Duration13.3 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Miejsce
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10300
Distinct (%)55.8%
Missing3507
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean4675.6803
Minimum1
Maximum10302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:54.275143image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile462
Q12307
median4613
Q36919
95-th percentile9377.55
Maximum10302
Range10301
Interquartile range (IQR)4612

Descriptive statistics

Standard deviation2768.8461
Coefficient of variation (CV)0.59218037
Kurtosis-1.0435152
Mean4675.6803
Median Absolute Deviation (MAD)2306
Skewness0.12232326
Sum86266302
Variance7666508.8
MonotonicityNot monotonic
2025-05-05T20:45:54.327322image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
< 0.1%
5431 2
 
< 0.1%
5444 2
 
< 0.1%
5443 2
 
< 0.1%
5442 2
 
< 0.1%
5441 2
 
< 0.1%
5440 2
 
< 0.1%
5439 2
 
< 0.1%
5438 2
 
< 0.1%
5437 2
 
< 0.1%
Other values (10290) 18430
83.9%
(Missing) 3507
 
16.0%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
10302 1
< 0.1%
10301 1
< 0.1%
10300 1
< 0.1%
10299 1
< 0.1%
10298 1
< 0.1%
10297 1
< 0.1%
10296 1
< 0.1%
10295 1
< 0.1%
10294 1
< 0.1%
10293 1
< 0.1%

Numer startowy
Real number (ℝ)

HIGH CORRELATION 

Distinct13767
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9131.0017
Minimum1
Maximum86990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:54.374085image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile629
Q12994
median5847
Q38771
95-th percentile27579.6
Maximum86990
Range86989
Interquartile range (IQR)5777

Descriptive statistics

Standard deviation14449.831
Coefficient of variation (CV)1.5825022
Kurtosis17.54962
Mean9131.0017
Median Absolute Deviation (MAD)2888
Skewness4.1178177
Sum2.004894 × 108
Variance2.0879761 × 108
MonotonicityNot monotonic
2025-05-05T20:45:54.419222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2984 2
 
< 0.1%
8420 2
 
< 0.1%
8730 2
 
< 0.1%
5362 2
 
< 0.1%
2701 2
 
< 0.1%
1735 2
 
< 0.1%
8728 2
 
< 0.1%
2866 2
 
< 0.1%
6473 2
 
< 0.1%
8823 2
 
< 0.1%
Other values (13757) 21937
99.9%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 1
< 0.1%
7 2
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 2
< 0.1%
ValueCountFrequency (%)
86990 1
< 0.1%
86970 1
< 0.1%
86959 1
< 0.1%
86946 1
< 0.1%
86938 1
< 0.1%
86937 1
< 0.1%
86936 1
< 0.1%
86931 1
< 0.1%
86927 1
< 0.1%
86926 1
< 0.1%

Imię
Text

Distinct975
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-05-05T20:45:54.515350image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length6.54839
Min length1

Characters and Unicode

Total characters143783
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique429 ?
Unique (%)2.0%

Sample

1st rowTOMASZ
2nd rowARKADIUSZ
3rd rowKRZYSZTOF
4th rowDAMIAN
5th rowKAMIL
ValueCountFrequency (%)
tomasz 800
 
3.6%
piotr 696
 
3.2%
marcin 681
 
3.1%
anonimowy 674
 
3.1%
michał 653
 
3.0%
paweł 642
 
2.9%
krzysztof 574
 
2.6%
łukasz 540
 
2.5%
mateusz 459
 
2.1%
jakub 436
 
2.0%
Other values (955) 15856
72.0%
2025-05-05T20:45:54.676377image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 26050
18.1%
R 10533
 
7.3%
I 9871
 
6.9%
N 8341
 
5.8%
E 7901
 
5.5%
M 7594
 
5.3%
O 7446
 
5.2%
Z 7283
 
5.1%
S 6528
 
4.5%
T 6489
 
4.5%
Other values (34) 45747
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 26050
18.1%
R 10533
 
7.3%
I 9871
 
6.9%
N 8341
 
5.8%
E 7901
 
5.5%
M 7594
 
5.3%
O 7446
 
5.2%
Z 7283
 
5.1%
S 6528
 
4.5%
T 6489
 
4.5%
Other values (34) 45747
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 26050
18.1%
R 10533
 
7.3%
I 9871
 
6.9%
N 8341
 
5.8%
E 7901
 
5.5%
M 7594
 
5.3%
O 7446
 
5.2%
Z 7283
 
5.1%
S 6528
 
4.5%
T 6489
 
4.5%
Other values (34) 45747
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 26050
18.1%
R 10533
 
7.3%
I 9871
 
6.9%
N 8341
 
5.8%
E 7901
 
5.5%
M 7594
 
5.3%
O 7446
 
5.2%
Z 7283
 
5.1%
S 6528
 
4.5%
T 6489
 
4.5%
Other values (34) 45747
31.8%
Distinct10274
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-05-05T20:45:54.784709image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Length

Max length25
Median length22
Mean length7.8397322
Min length2

Characters and Unicode

Total characters172137
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5683 ?
Unique (%)25.9%

Sample

1st rowGRYCKO
2nd rowGARDZIELEWSKI
3rd rowHADAS
4th rowDYDUCH
5th rowMAŃKOWSKI
ValueCountFrequency (%)
zawodnik 699
 
3.2%
nowak 86
 
0.4%
wójcik 61
 
0.3%
mazur 54
 
0.2%
kowalczyk 51
 
0.2%
kaczmarek 49
 
0.2%
kowalski 46
 
0.2%
woźniak 35
 
0.2%
wieczorek 32
 
0.1%
adamczyk 32
 
0.1%
Other values (10271) 20917
94.8%
2025-05-05T20:45:54.934007image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 19245
 
11.2%
A 18955
 
11.0%
I 15707
 
9.1%
S 11951
 
6.9%
O 11192
 
6.5%
Z 9659
 
5.6%
E 8880
 
5.2%
R 8619
 
5.0%
W 8561
 
5.0%
C 8033
 
4.7%
Other values (39) 51335
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 172137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 19245
 
11.2%
A 18955
 
11.0%
I 15707
 
9.1%
S 11951
 
6.9%
O 11192
 
6.5%
Z 9659
 
5.6%
E 8880
 
5.2%
R 8619
 
5.0%
W 8561
 
5.0%
C 8033
 
4.7%
Other values (39) 51335
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 172137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 19245
 
11.2%
A 18955
 
11.0%
I 15707
 
9.1%
S 11951
 
6.9%
O 11192
 
6.5%
Z 9659
 
5.6%
E 8880
 
5.2%
R 8619
 
5.0%
W 8561
 
5.0%
C 8033
 
4.7%
Other values (39) 51335
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 172137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 19245
 
11.2%
A 18955
 
11.0%
I 15707
 
9.1%
S 11951
 
6.9%
O 11192
 
6.5%
Z 9659
 
5.6%
E 8880
 
5.2%
R 8619
 
5.0%
W 8561
 
5.0%
C 8033
 
4.7%
Other values (39) 51335
29.8%

Miasto
Text

MISSING 

Distinct2170
Distinct (%)12.2%
Missing4183
Missing (%)19.1%
Memory size1.7 MiB
2025-05-05T20:45:55.052975image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Length

Max length30
Median length29
Mean length8.030719
Min length1

Characters and Unicode

Total characters142738
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1011 ?
Unique (%)5.7%

Sample

1st rowWROCŁAW
2nd rowPOZNAŃ
3rd rowKĘPNO
4th rowMIRKÓW
5th rowWROCŁAW
ValueCountFrequency (%)
wrocław 6014
30.8%
warszawa 552
 
2.8%
poznań 352
 
1.8%
kraków 269
 
1.4%
wroclaw 225
 
1.2%
góra 202
 
1.0%
legnica 149
 
0.8%
oleśnica 139
 
0.7%
opole 134
 
0.7%
łódź 116
 
0.6%
Other values (2206) 11352
58.2%
2025-05-05T20:45:55.220324image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 19352
13.6%
A 15734
11.0%
O 13696
 
9.6%
R 11604
 
8.1%
C 11365
 
8.0%
Ł 7945
 
5.6%
I 7817
 
5.5%
E 6702
 
4.7%
Z 5758
 
4.0%
K 5037
 
3.5%
Other values (46) 37728
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 142738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 19352
13.6%
A 15734
11.0%
O 13696
 
9.6%
R 11604
 
8.1%
C 11365
 
8.0%
Ł 7945
 
5.6%
I 7817
 
5.5%
E 6702
 
4.7%
Z 5758
 
4.0%
K 5037
 
3.5%
Other values (46) 37728
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 142738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 19352
13.6%
A 15734
11.0%
O 13696
 
9.6%
R 11604
 
8.1%
C 11365
 
8.0%
Ł 7945
 
5.6%
I 7817
 
5.5%
E 6702
 
4.7%
Z 5758
 
4.0%
K 5037
 
3.5%
Other values (46) 37728
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 142738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 19352
13.6%
A 15734
11.0%
O 13696
 
9.6%
R 11604
 
8.1%
C 11365
 
8.0%
Ł 7945
 
5.6%
I 7817
 
5.5%
E 6702
 
4.7%
Z 5758
 
4.0%
K 5037
 
3.5%
Other values (46) 37728
26.4%

Kraj
Categorical

IMBALANCE  MISSING 

Distinct43
Distinct (%)0.2%
Missing3507
Missing (%)16.0%
Memory size1.2 MiB
POL
18038 
GER
 
89
UKR
 
48
GBR
 
42
BLR
 
26
Other values (38)
 
207

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters55350
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.1%

Sample

1st rowPOL
2nd rowPOL
3rd rowPOL
4th rowPOL
5th rowPOL

Common Values

ValueCountFrequency (%)
POL 18038
82.2%
GER 89
 
0.4%
UKR 48
 
0.2%
GBR 42
 
0.2%
BLR 26
 
0.1%
AUT 23
 
0.1%
ITA 20
 
0.1%
ESP 19
 
0.1%
GRE 18
 
0.1%
CZE 18
 
0.1%
Other values (33) 109
 
0.5%
(Missing) 3507
 
16.0%

Length

2025-05-05T20:45:55.279689image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pol 18038
97.8%
ger 89
 
0.5%
ukr 48
 
0.3%
gbr 42
 
0.2%
blr 26
 
0.1%
aut 23
 
0.1%
ita 20
 
0.1%
esp 19
 
0.1%
gre 18
 
0.1%
cze 18
 
0.1%
Other values (33) 109
 
0.6%

Most occurring characters

ValueCountFrequency (%)
L 18086
32.7%
P 18061
32.6%
O 18048
32.6%
R 256
 
0.5%
E 183
 
0.3%
G 162
 
0.3%
U 96
 
0.2%
B 80
 
0.1%
A 74
 
0.1%
K 56
 
0.1%
Other values (13) 248
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 18086
32.7%
P 18061
32.6%
O 18048
32.6%
R 256
 
0.5%
E 183
 
0.3%
G 162
 
0.3%
U 96
 
0.2%
B 80
 
0.1%
A 74
 
0.1%
K 56
 
0.1%
Other values (13) 248
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 18086
32.7%
P 18061
32.6%
O 18048
32.6%
R 256
 
0.5%
E 183
 
0.3%
G 162
 
0.3%
U 96
 
0.2%
B 80
 
0.1%
A 74
 
0.1%
K 56
 
0.1%
Other values (13) 248
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 18086
32.7%
P 18061
32.6%
O 18048
32.6%
R 256
 
0.5%
E 183
 
0.3%
G 162
 
0.3%
U 96
 
0.2%
B 80
 
0.1%
A 74
 
0.1%
K 56
 
0.1%
Other values (13) 248
 
0.4%

Drużyna
Text

MISSING 

Distinct4035
Distinct (%)48.0%
Missing13555
Missing (%)61.7%
Memory size1.1 MiB
2025-05-05T20:45:55.380156image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Length

Max length84
Median length48
Mean length14.570459
Min length1

Characters and Unicode

Total characters122421
Distinct characters110
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2654 ?
Unique (%)31.6%

Sample

1st rowUKS BLIZA WŁADYSŁAWOWO
2nd rowARKADIUSZGARDZIELEWSKI.PL
3rd rowAZS POLITECHNIKA OPOLSKA
4th rowPARKRUN WROCŁAW
5th rowWOSIEK TEAM KS AZS AWF WROCŁAW
ValueCountFrequency (%)
team 1290
 
7.0%
wrocław 364
 
2.0%
running 321
 
1.7%
240
 
1.3%
biega 223
 
1.2%
runners 212
 
1.1%
brak 202
 
1.1%
run 192
 
1.0%
kb 179
 
1.0%
grupa 134
 
0.7%
Other values (4519) 15140
81.9%
2025-05-05T20:45:55.550316image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 10548
 
8.6%
10095
 
8.2%
E 7871
 
6.4%
I 7124
 
5.8%
R 6464
 
5.3%
O 6108
 
5.0%
N 5934
 
4.8%
T 4968
 
4.1%
S 4802
 
3.9%
K 4402
 
3.6%
Other values (100) 54105
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10548
 
8.6%
10095
 
8.2%
E 7871
 
6.4%
I 7124
 
5.8%
R 6464
 
5.3%
O 6108
 
5.0%
N 5934
 
4.8%
T 4968
 
4.1%
S 4802
 
3.9%
K 4402
 
3.6%
Other values (100) 54105
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10548
 
8.6%
10095
 
8.2%
E 7871
 
6.4%
I 7124
 
5.8%
R 6464
 
5.3%
O 6108
 
5.0%
N 5934
 
4.8%
T 4968
 
4.1%
S 4802
 
3.9%
K 4402
 
3.6%
Other values (100) 54105
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10548
 
8.6%
10095
 
8.2%
E 7871
 
6.4%
I 7124
 
5.8%
R 6464
 
5.3%
O 6108
 
5.0%
N 5934
 
4.8%
T 4968
 
4.1%
S 4802
 
3.9%
K 4402
 
3.6%
Other values (100) 54105
44.2%

Płeć
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing11
Missing (%)0.1%
Memory size1.2 MiB
M
15339 
K
6607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21946
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 15339
69.9%
K 6607
30.1%
(Missing) 11
 
0.1%

Length

2025-05-05T20:45:55.605241image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T20:45:55.642761image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
m 15339
69.9%
k 6607
30.1%

Most occurring characters

ValueCountFrequency (%)
M 15339
69.9%
K 6607
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 15339
69.9%
K 6607
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 15339
69.9%
K 6607
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 15339
69.9%
K 6607
30.1%

Płeć Miejsce
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7238
Distinct (%)39.2%
Missing3507
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean2741.1376
Minimum1
Maximum7240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:55.680366image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile231
Q11154
median2307
Q34227.75
95-th percentile6315.55
Maximum7240
Range7239
Interquartile range (IQR)3073.75

Descriptive statistics

Standard deviation1913.8757
Coefficient of variation (CV)0.69820491
Kurtosis-0.78594016
Mean2741.1376
Median Absolute Deviation (MAD)1409
Skewness0.53073354
Sum50573989
Variance3662920.3
MonotonicityNot monotonic
2025-05-05T20:45:55.726194image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
< 0.1%
1553 4
 
< 0.1%
1546 4
 
< 0.1%
1547 4
 
< 0.1%
1548 4
 
< 0.1%
1549 4
 
< 0.1%
1550 4
 
< 0.1%
1551 4
 
< 0.1%
1552 4
 
< 0.1%
1554 4
 
< 0.1%
Other values (7228) 18410
83.8%
(Missing) 3507
 
16.0%
ValueCountFrequency (%)
1 4
< 0.1%
2 4
< 0.1%
3 4
< 0.1%
4 4
< 0.1%
5 4
< 0.1%
6 4
< 0.1%
7 4
< 0.1%
8 4
< 0.1%
9 4
< 0.1%
10 4
< 0.1%
ValueCountFrequency (%)
7240 1
< 0.1%
7239 1
< 0.1%
7238 1
< 0.1%
7237 1
< 0.1%
7236 1
< 0.1%
7235 1
< 0.1%
7234 1
< 0.1%
7233 1
< 0.1%
7232 1
< 0.1%
7231 1
< 0.1%

Kategoria wiekowa
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing31
Missing (%)0.1%
Memory size1.3 MiB
M40
5157 
M30
4953 
M20
2736 
K30
2357 
K40
2224 
Other values (8)
4499 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters65778
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM30
2nd rowM30
3rd rowM20
4th rowM30
5th rowM20

Common Values

ValueCountFrequency (%)
M40 5157
23.5%
M30 4953
22.6%
M20 2736
12.5%
K30 2357
10.7%
K40 2224
10.1%
M50 1710
 
7.8%
K20 1373
 
6.3%
M60 643
 
2.9%
K50 519
 
2.4%
M70 116
 
0.5%
Other values (3) 138
 
0.6%

Length

2025-05-05T20:45:55.767229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m40 5157
23.5%
m30 4953
22.6%
m20 2736
12.5%
k30 2357
10.7%
k40 2224
10.1%
m50 1710
 
7.8%
k20 1373
 
6.3%
m60 643
 
2.9%
k50 519
 
2.4%
m70 116
 
0.5%
Other values (3) 138
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 21926
33.3%
M 15320
23.3%
4 7381
 
11.2%
3 7310
 
11.1%
K 6606
 
10.0%
2 4109
 
6.2%
5 2229
 
3.4%
6 757
 
1.2%
7 135
 
0.2%
8 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21926
33.3%
M 15320
23.3%
4 7381
 
11.2%
3 7310
 
11.1%
K 6606
 
10.0%
2 4109
 
6.2%
5 2229
 
3.4%
6 757
 
1.2%
7 135
 
0.2%
8 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21926
33.3%
M 15320
23.3%
4 7381
 
11.2%
3 7310
 
11.1%
K 6606
 
10.0%
2 4109
 
6.2%
5 2229
 
3.4%
6 757
 
1.2%
7 135
 
0.2%
8 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21926
33.3%
M 15320
23.3%
4 7381
 
11.2%
3 7310
 
11.1%
K 6606
 
10.0%
2 4109
 
6.2%
5 2229
 
3.4%
6 757
 
1.2%
7 135
 
0.2%
8 5
 
< 0.1%

Kategoria wiekowa Miejsce
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2387
Distinct (%)13.0%
Missing3527
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean734.75735
Minimum1
Maximum2388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:55.805558image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42
Q1251
median589
Q31071.75
95-th percentile1927
Maximum2388
Range2387
Interquartile range (IQR)820.75

Descriptive statistics

Standard deviation590.14828
Coefficient of variation (CV)0.80318798
Kurtosis-0.16086716
Mean734.75735
Median Absolute Deviation (MAD)381
Skewness0.86191208
Sum13541578
Variance348274.99
MonotonicityNot monotonic
2025-05-05T20:45:55.852441image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 26
 
0.1%
2 25
 
0.1%
3 24
 
0.1%
4 24
 
0.1%
5 24
 
0.1%
6 23
 
0.1%
29 22
 
0.1%
24 22
 
0.1%
25 22
 
0.1%
26 22
 
0.1%
Other values (2377) 18196
82.9%
(Missing) 3527
 
16.1%
ValueCountFrequency (%)
1 26
0.1%
2 25
0.1%
3 24
0.1%
4 24
0.1%
5 24
0.1%
6 23
0.1%
7 22
0.1%
8 22
0.1%
9 22
0.1%
10 22
0.1%
ValueCountFrequency (%)
2388 1
< 0.1%
2387 1
< 0.1%
2386 1
< 0.1%
2385 1
< 0.1%
2384 1
< 0.1%
2383 1
< 0.1%
2382 1
< 0.1%
2381 1
< 0.1%
2380 1
< 0.1%
2379 1
< 0.1%

Rocznik
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct66
Distinct (%)0.3%
Missing485
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1981.4499
Minimum0
Maximum2006
Zeros31
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:55.899193image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1966
Q11978
median1985
Q31992
95-th percentile2000
Maximum2006
Range2006
Interquartile range (IQR)14

Descriptive statistics

Standard deviation76.047837
Coefficient of variation (CV)0.038379893
Kurtosis662.6044
Mean1981.4499
Median Absolute Deviation (MAD)7
Skewness-25.540099
Sum42545693
Variance5783.2734
MonotonicityNot monotonic
2025-05-05T20:45:55.944358image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1981 872
 
4.0%
1983 851
 
3.9%
1982 834
 
3.8%
1990 784
 
3.6%
1986 780
 
3.6%
1984 780
 
3.6%
1980 766
 
3.5%
1979 763
 
3.5%
1985 757
 
3.4%
1978 739
 
3.4%
Other values (56) 13546
61.7%
ValueCountFrequency (%)
0 31
0.1%
1934 2
 
< 0.1%
1943 2
 
< 0.1%
1944 2
 
< 0.1%
1945 1
 
< 0.1%
1946 6
 
< 0.1%
1947 3
 
< 0.1%
1948 8
 
< 0.1%
1949 22
0.1%
1950 14
0.1%
ValueCountFrequency (%)
2006 24
 
0.1%
2005 84
 
0.4%
2004 114
 
0.5%
2003 181
 
0.8%
2002 219
1.0%
2001 297
1.4%
2000 364
1.7%
1999 348
1.6%
1998 457
2.1%
1997 512
2.3%

5 km Czas
Date

MISSING 

Distinct1331
Distinct (%)7.2%
Missing3546
Missing (%)16.1%
Memory size171.7 KiB
Minimum2025-05-05 00:00:00
Maximum2025-05-05 01:03:45
2025-05-05T20:45:56.095387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:56.141485image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

5 km Miejsce Open
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10326
Distinct (%)56.1%
Missing3546
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean4684.6776
Minimum1
Maximum10353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:56.192009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile464.5
Q12311.5
median4619
Q36927
95-th percentile9410.5
Maximum10353
Range10352
Interquartile range (IQR)4615.5

Descriptive statistics

Standard deviation2776.0857
Coefficient of variation (CV)0.59258842
Kurtosis-1.0369425
Mean4684.6776
Median Absolute Deviation (MAD)2308
Skewness0.12759154
Sum86249600
Variance7706651.9
MonotonicityNot monotonic
2025-05-05T20:45:56.238251image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
< 0.1%
5458 2
 
< 0.1%
6570 2
 
< 0.1%
5109 2
 
< 0.1%
5450 2
 
< 0.1%
4762 2
 
< 0.1%
3769 2
 
< 0.1%
5473 2
 
< 0.1%
5922 2
 
< 0.1%
4812 2
 
< 0.1%
Other values (10316) 18391
83.8%
(Missing) 3546
 
16.1%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
10353 1
< 0.1%
10352 1
< 0.1%
10350 1
< 0.1%
10345 1
< 0.1%
10344 1
< 0.1%
10343 1
< 0.1%
10342 1
< 0.1%
10341 1
< 0.1%
10340 1
< 0.1%
10339 1
< 0.1%

5 km Tempo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1331
Distinct (%)7.2%
Missing3546
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean5.5863402
Minimum0
Maximum12.75
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:56.283416image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2783333
Q15.0166667
median5.5433333
Q36.125
95-th percentile6.99
Maximum12.75
Range12.75
Interquartile range (IQR)1.1083333

Descriptive statistics

Standard deviation0.8254512
Coefficient of variation (CV)0.14776243
Kurtosis0.73271139
Mean5.5863402
Median Absolute Deviation (MAD)0.55333333
Skewness0.31000869
Sum102850.11
Variance0.68136968
MonotonicityNot monotonic
2025-05-05T20:45:56.331270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.213333333 53
 
0.2%
5.48 48
 
0.2%
5.096666667 45
 
0.2%
5.606666667 45
 
0.2%
5.316666667 43
 
0.2%
5.32 43
 
0.2%
5.466666667 43
 
0.2%
5.673333333 42
 
0.2%
5.456666667 42
 
0.2%
5.363333333 42
 
0.2%
Other values (1321) 17965
81.8%
(Missing) 3546
 
16.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
2.923333333 1
 
< 0.1%
2.96 1
 
< 0.1%
3.02 3
< 0.1%
3.023333333 1
 
< 0.1%
3.076666667 1
 
< 0.1%
3.123333333 1
 
< 0.1%
3.153333333 1
 
< 0.1%
3.156666667 1
 
< 0.1%
3.23 1
 
< 0.1%
ValueCountFrequency (%)
12.75 1
< 0.1%
11.55666667 1
< 0.1%
10.6 1
< 0.1%
10.50666667 1
< 0.1%
10.04666667 1
< 0.1%
9.866666667 1
< 0.1%
9.36 1
< 0.1%
9.06 1
< 0.1%
9.003333333 1
< 0.1%
8.876666667 1
< 0.1%

10 km Czas
Date

MISSING 

Distinct2497
Distinct (%)13.6%
Missing3530
Missing (%)16.1%
Memory size171.7 KiB
Minimum2025-05-05 00:29:15
Maximum2025-05-05 01:43:28
2025-05-05T20:45:56.378043image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:56.424195image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

10 km Miejsce Open
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10313
Distinct (%)56.0%
Missing3530
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean4681.818
Minimum1
Maximum10330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:56.470308image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile465
Q12309
median4617
Q36927
95-th percentile9396.7
Maximum10330
Range10329
Interquartile range (IQR)4618

Descriptive statistics

Standard deviation2772.9986
Coefficient of variation (CV)0.59229098
Kurtosis-1.0414928
Mean4681.818
Median Absolute Deviation (MAD)2309
Skewness0.12445543
Sum86271860
Variance7689521
MonotonicityNot monotonic
2025-05-05T20:45:56.516000image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
< 0.1%
5718 2
 
< 0.1%
5076 2
 
< 0.1%
3616 2
 
< 0.1%
5544 2
 
< 0.1%
5925 2
 
< 0.1%
4785 2
 
< 0.1%
4468 2
 
< 0.1%
5245 2
 
< 0.1%
4977 2
 
< 0.1%
Other values (10303) 18407
83.8%
(Missing) 3530
 
16.1%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
10330 1
< 0.1%
10329 1
< 0.1%
10328 1
< 0.1%
10324 1
< 0.1%
10323 1
< 0.1%
10322 1
< 0.1%
10321 1
< 0.1%
10320 1
< 0.1%
10319 1
< 0.1%
10318 1
< 0.1%

10 km Tempo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1454
Distinct (%)7.9%
Missing3562
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean5.5720674
Minimum2.92
Maximum11.346667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:56.561979image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2.92
5-th percentile4.2166667
Q14.94
median5.4866667
Q36.11
95-th percentile7.1566667
Maximum11.346667
Range8.4266667
Interquartile range (IQR)1.17

Descriptive statistics

Standard deviation0.8989413
Coefficient of variation (CV)0.16132994
Kurtosis0.58744493
Mean5.5720674
Median Absolute Deviation (MAD)0.58
Skewness0.53573297
Sum102498.18
Variance0.80809546
MonotonicityNot monotonic
2025-05-05T20:45:56.604642image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.366666667 48
 
0.2%
5.506666667 45
 
0.2%
5.483333333 45
 
0.2%
5.36 43
 
0.2%
5.626666667 41
 
0.2%
5.273333333 41
 
0.2%
5.426666667 40
 
0.2%
5.423333333 40
 
0.2%
5.316666667 40
 
0.2%
5.306666667 40
 
0.2%
Other values (1444) 17972
81.9%
(Missing) 3562
 
16.2%
ValueCountFrequency (%)
2.92 3
< 0.1%
2.926666667 1
 
< 0.1%
2.983333333 1
 
< 0.1%
3.11 1
 
< 0.1%
3.123333333 1
 
< 0.1%
3.153333333 1
 
< 0.1%
3.16 1
 
< 0.1%
3.196666667 1
 
< 0.1%
3.233333333 2
< 0.1%
3.25 1
 
< 0.1%
ValueCountFrequency (%)
11.34666667 1
< 0.1%
10.64666667 1
< 0.1%
10.40666667 1
< 0.1%
10.25 1
< 0.1%
10.13333333 1
< 0.1%
9.87 1
< 0.1%
9.796666667 1
< 0.1%
9.753333333 1
< 0.1%
9.533333333 1
< 0.1%
9.343333333 2
< 0.1%

15 km Czas
Date

MISSING 

Distinct3650
Distinct (%)19.8%
Missing3529
Missing (%)16.1%
Memory size171.7 KiB
Minimum2025-05-05 00:44:47
Maximum2025-05-05 02:34:09
2025-05-05T20:45:56.647957image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:56.694963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

15 km Miejsce Open
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10300
Distinct (%)55.9%
Missing3529
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean4677.3321
Minimum1
Maximum10305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:56.743840image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile464
Q12309
median4613.5
Q36920.25
95-th percentile9380.65
Maximum10305
Range10304
Interquartile range (IQR)4611.25

Descriptive statistics

Standard deviation2768.8499
Coefficient of variation (CV)0.59197206
Kurtosis-1.0432973
Mean4677.3321
Median Absolute Deviation (MAD)2305.5
Skewness0.12254811
Sum86193876
Variance7666529.8
MonotonicityNot monotonic
2025-05-05T20:45:56.789429image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
< 0.1%
5219 2
 
< 0.1%
5294 2
 
< 0.1%
5631 2
 
< 0.1%
5611 2
 
< 0.1%
5411 2
 
< 0.1%
5284 2
 
< 0.1%
3863 2
 
< 0.1%
5416 2
 
< 0.1%
5826 2
 
< 0.1%
Other values (10290) 18408
83.8%
(Missing) 3529
 
16.1%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
10305 1
< 0.1%
10304 1
< 0.1%
10303 1
< 0.1%
10302 1
< 0.1%
10301 1
< 0.1%
10300 1
< 0.1%
10299 1
< 0.1%
10298 1
< 0.1%
10297 1
< 0.1%
10296 1
< 0.1%

15 km Tempo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1624
Distinct (%)8.8%
Missing3544
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean5.9007866
Minimum3.0833333
Maximum11.213333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:56.834977image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.0833333
5-th percentile4.4233333
Q15.1933333
median5.7666667
Q36.5033333
95-th percentile7.7833333
Maximum11.213333
Range8.13
Interquartile range (IQR)1.31

Descriptive statistics

Standard deviation1.0126261
Coefficient of variation (CV)0.17160866
Kurtosis0.47327685
Mean5.9007866
Median Absolute Deviation (MAD)0.64333333
Skewness0.613667
Sum108651.18
Variance1.0254116
MonotonicityNot monotonic
2025-05-05T20:45:56.881288image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.766666667 44
 
0.2%
5.726666667 40
 
0.2%
5.71 40
 
0.2%
5.68 40
 
0.2%
5.486666667 39
 
0.2%
5.723333333 39
 
0.2%
5.516666667 39
 
0.2%
5.66 38
 
0.2%
5.543333333 38
 
0.2%
5.576666667 37
 
0.2%
Other values (1614) 18019
82.1%
(Missing) 3544
 
16.1%
ValueCountFrequency (%)
3.083333333 3
< 0.1%
3.106666667 1
 
< 0.1%
3.143333333 1
 
< 0.1%
3.236666667 1
 
< 0.1%
3.293333333 1
 
< 0.1%
3.303333333 1
 
< 0.1%
3.33 1
 
< 0.1%
3.336666667 1
 
< 0.1%
3.363333333 1
 
< 0.1%
3.376666667 1
 
< 0.1%
ValueCountFrequency (%)
11.21333333 1
< 0.1%
10.44333333 1
< 0.1%
10.35 1
< 0.1%
10.32666667 1
< 0.1%
10.2 1
< 0.1%
10.17 1
< 0.1%
10.13666667 1
< 0.1%
10.11666667 1
< 0.1%
9.986666667 1
< 0.1%
9.98 1
< 0.1%

20 km Czas
Date

MISSING 

Distinct4825
Distinct (%)26.2%
Missing3518
Missing (%)16.0%
Memory size171.7 KiB
Minimum2025-05-05 01:00:33
Maximum2025-05-05 03:21:22
2025-05-05T20:45:56.931392image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:56.974514image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

20 km Miejsce Open
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10301
Distinct (%)55.9%
Missing3518
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean4676.54
Minimum1
Maximum10306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:57.018672image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile462
Q12308
median4614
Q36919.5
95-th percentile9382.1
Maximum10306
Range10305
Interquartile range (IQR)4611.5

Descriptive statistics

Standard deviation2769.4308
Coefficient of variation (CV)0.59219653
Kurtosis-1.0426614
Mean4676.54
Median Absolute Deviation (MAD)2306
Skewness0.12272299
Sum86230721
Variance7669746.7
MonotonicityNot monotonic
2025-05-05T20:45:57.064634image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
< 0.1%
5381 2
 
< 0.1%
5451 2
 
< 0.1%
5515 2
 
< 0.1%
5473 2
 
< 0.1%
5404 2
 
< 0.1%
5375 2
 
< 0.1%
5270 2
 
< 0.1%
5403 2
 
< 0.1%
5492 2
 
< 0.1%
Other values (10291) 18419
83.9%
(Missing) 3518
 
16.0%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
10306 1
< 0.1%
10305 1
< 0.1%
10304 1
< 0.1%
10303 1
< 0.1%
10302 1
< 0.1%
10301 1
< 0.1%
10300 1
< 0.1%
10299 1
< 0.1%
10298 1
< 0.1%
10297 1
< 0.1%

20 km Tempo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1877
Distinct (%)10.2%
Missing3535
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean6.3427487
Minimum3.0866667
Maximum14.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:57.109891image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.0866667
5-th percentile4.65
Q15.49
median6.14
Q37.02
95-th percentile8.6733333
Maximum14.94
Range11.853333
Interquartile range (IQR)1.53

Descriptive statistics

Standard deviation1.2229785
Coefficient of variation (CV)0.19281522
Kurtosis0.86252693
Mean6.3427487
Median Absolute Deviation (MAD)0.74
Skewness0.80615737
Sum116846.12
Variance1.4956764
MonotonicityNot monotonic
2025-05-05T20:45:57.157146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.833333333 36
 
0.2%
5.55 36
 
0.2%
5.733333333 35
 
0.2%
5.796666667 35
 
0.2%
5.806666667 34
 
0.2%
5.93 34
 
0.2%
6.39 33
 
0.2%
5.68 33
 
0.2%
5.776666667 33
 
0.2%
5.98 33
 
0.2%
Other values (1867) 18080
82.3%
(Missing) 3535
 
16.1%
ValueCountFrequency (%)
3.086666667 1
< 0.1%
3.103333333 1
< 0.1%
3.173333333 1
< 0.1%
3.386666667 1
< 0.1%
3.393333333 1
< 0.1%
3.44 1
< 0.1%
3.486666667 1
< 0.1%
3.516666667 1
< 0.1%
3.533333333 2
< 0.1%
3.54 1
< 0.1%
ValueCountFrequency (%)
14.94 1
< 0.1%
13.47333333 1
< 0.1%
12.67333333 1
< 0.1%
12.25 1
< 0.1%
11.89666667 1
< 0.1%
11.88333333 1
< 0.1%
11.76 1
< 0.1%
11.69666667 1
< 0.1%
11.67333333 1
< 0.1%
11.65333333 1
< 0.1%

Tempo Stabilność
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7359
Distinct (%)40.0%
Missing3580
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean0.052152281
Minimum-0.34533333
Maximum0.62953333
Zeros1
Zeros (%)< 0.1%
Negative1237
Negative (%)5.6%
Memory size171.7 KiB
2025-05-05T20:45:57.204880image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-0.34533333
5-th percentile-0.0038
Q10.020533333
median0.041866667
Q30.073333333
95-th percentile0.14426667
Maximum0.62953333
Range0.97486667
Interquartile range (IQR)0.0528

Descriptive statistics

Standard deviation0.047736697
Coefficient of variation (CV)0.91533288
Kurtosis5.6331553
Mean0.052152281
Median Absolute Deviation (MAD)0.024733333
Skewness1.5169552
Sum958.40247
Variance0.0022787923
MonotonicityNot monotonic
2025-05-05T20:45:57.248667image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0262 16
 
0.1%
0.0266 15
 
0.1%
0.01446666667 15
 
0.1%
0.03686666667 15
 
0.1%
0.02566666667 15
 
0.1%
0.01713333333 15
 
0.1%
0.02853333333 14
 
0.1%
0.03133333333 14
 
0.1%
0.033 13
 
0.1%
0.03906666667 13
 
0.1%
Other values (7349) 18232
83.0%
(Missing) 3580
 
16.3%
ValueCountFrequency (%)
-0.3453333333 1
< 0.1%
-0.1278 1
< 0.1%
-0.1176 1
< 0.1%
-0.1023333333 1
< 0.1%
-0.1021333333 1
< 0.1%
-0.102 1
< 0.1%
-0.08766666667 1
< 0.1%
-0.08306666667 1
< 0.1%
-0.07586666667 1
< 0.1%
-0.0754 1
< 0.1%
ValueCountFrequency (%)
0.6295333333 1
< 0.1%
0.5289333333 1
< 0.1%
0.4361333333 1
< 0.1%
0.4259333333 1
< 0.1%
0.4219333333 1
< 0.1%
0.4052666667 1
< 0.1%
0.3966 1
< 0.1%
0.3951333333 1
< 0.1%
0.3739333333 1
< 0.1%
0.3638 1
< 0.1%

Czas
Text

MISSING 

Distinct5042
Distinct (%)25.3%
Missing2055
Missing (%)9.4%
Memory size1.3 MiB
2025-05-05T20:45:57.338793image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.6352125
Min length3

Characters and Unicode

Total characters151956
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1291 ?
Unique (%)6.5%

Sample

1st row01:04:59
2nd row01:06:23
3rd row01:08:24
4th row01:10:16
5th row01:10:27
ValueCountFrequency (%)
dns 1332
 
6.7%
dnf 120
 
0.6%
01:59:43 16
 
0.1%
02:10:23 16
 
0.1%
01:58:24 14
 
0.1%
02:05:21 14
 
0.1%
02:01:25 14
 
0.1%
01:55:25 14
 
0.1%
01:56:06 13
 
0.1%
02:04:09 13
 
0.1%
Other values (5032) 18336
92.1%
2025-05-05T20:45:57.484480image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 36900
24.3%
0 28725
18.9%
2 18413
12.1%
1 18105
11.9%
5 10896
 
7.2%
4 10355
 
6.8%
3 9218
 
6.1%
9 3934
 
2.6%
8 3753
 
2.5%
7 3685
 
2.4%
Other values (5) 7972
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 36900
24.3%
0 28725
18.9%
2 18413
12.1%
1 18105
11.9%
5 10896
 
7.2%
4 10355
 
6.8%
3 9218
 
6.1%
9 3934
 
2.6%
8 3753
 
2.5%
7 3685
 
2.4%
Other values (5) 7972
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 36900
24.3%
0 28725
18.9%
2 18413
12.1%
1 18105
11.9%
5 10896
 
7.2%
4 10355
 
6.8%
3 9218
 
6.1%
9 3934
 
2.6%
8 3753
 
2.5%
7 3685
 
2.4%
Other values (5) 7972
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 36900
24.3%
0 28725
18.9%
2 18413
12.1%
1 18105
11.9%
5 10896
 
7.2%
4 10355
 
6.8%
3 9218
 
6.1%
9 3934
 
2.6%
8 3753
 
2.5%
7 3685
 
2.4%
Other values (5) 7972
 
5.2%

Tempo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5040
Distinct (%)27.3%
Missing3507
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean5.852857
Minimum3.0362645
Maximum10.076637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.7 KiB
2025-05-05T20:45:57.548974image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.0362645
5-th percentile4.4181086
Q15.1789524
median5.7327961
Q36.4420874
95-th percentile7.6021964
Maximum10.076637
Range7.0403729
Interquartile range (IQR)1.263135

Descriptive statistics

Standard deviation0.96169181
Coefficient of variation (CV)0.16431152
Kurtosis0.21337021
Mean5.852857
Median Absolute Deviation (MAD)0.61625978
Skewness0.48851578
Sum107985.21
Variance0.92485114
MonotonicityNot monotonic
2025-05-05T20:45:57.595221image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.675120487 16
 
0.1%
6.180769535 16
 
0.1%
5.755708304 14
 
0.1%
5.94216639 14
 
0.1%
5.471280714 14
 
0.1%
5.612704432 14
 
0.1%
5.690131943 13
 
0.1%
5.682231176 13
 
0.1%
5.957177846 13
 
0.1%
5.66247926 13
 
0.1%
Other values (5030) 18310
83.4%
(Missing) 3507
 
16.0%
ValueCountFrequency (%)
3.036264518 1
< 0.1%
3.052856127 1
< 0.1%
3.065497353 1
< 0.1%
3.080508809 1
< 0.1%
3.146875247 1
< 0.1%
3.24247452 1
< 0.1%
3.305680651 1
< 0.1%
3.32227226 1
< 0.1%
3.330963103 1
< 0.1%
3.332543257 1
< 0.1%
ValueCountFrequency (%)
10.07663743 1
< 0.1%
9.885438888 1
< 0.1%
9.775618235 1
< 0.1%
9.64762582 1
< 0.1%
9.593110532 1
< 0.1%
9.589950225 1
< 0.1%
9.572568539 1
< 0.1%
9.566247926 1
< 0.1%
9.556767006 1
< 0.1%
9.534644861 1
< 0.1%

rok
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024
13007 
2023
8950 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters87828
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2024 13007
59.2%
2023 8950
40.8%

Length

2025-05-05T20:45:57.742172image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T20:45:57.779267image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
2024 13007
59.2%
2023 8950
40.8%

Most occurring characters

ValueCountFrequency (%)
2 43914
50.0%
0 21957
25.0%
4 13007
 
14.8%
3 8950
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 43914
50.0%
0 21957
25.0%
4 13007
 
14.8%
3 8950
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 43914
50.0%
0 21957
25.0%
4 13007
 
14.8%
3 8950
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 43914
50.0%
0 21957
25.0%
4 13007
 
14.8%
3 8950
 
10.2%

Interactions

2025-05-05T20:45:52.893882image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:43.976764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:44.669569image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:45.326262image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:45.961473image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:46.571389image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:47.200616image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:47.907040image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:48.554327image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:49.158153image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:49.733585image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:50.338890image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:51.102336image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:51.703236image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:52.298602image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:52.933773image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:44.015247image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:44.709369image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:45.368174image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:46.003594image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:46.612186image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:47.341512image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:47.946807image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:48.592362image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:49.195942image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2025-05-05T20:45:49.773014image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2025-05-05T20:45:52.852859image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2025-05-05T20:45:57.817419image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
10 km Miejsce Open10 km Tempo15 km Miejsce Open15 km Tempo20 km Miejsce Open20 km Tempo5 km Miejsce Open5 km TempoKategoria wiekowaKategoria wiekowa MiejsceKrajMiejsceNumer startowyPłećPłeć MiejsceRocznikTempoTempo Stabilnośćrok
10 km Miejsce Open1.0000.9770.9930.9440.9760.8500.9910.9830.1280.4900.0250.9720.5080.3500.614-0.0230.9570.2630.314
10 km Tempo0.9771.0000.9790.9700.9670.9030.9510.9650.1430.4830.0210.9640.4460.3360.607-0.0380.9790.3760.034
15 km Miejsce Open0.9930.9791.0000.9710.9920.8840.9750.9670.1260.5050.0210.9890.5020.3440.632-0.0240.9750.3380.315
15 km Tempo0.9440.9700.9711.0000.9790.9470.9120.9230.1280.5050.0220.9780.4350.3260.634-0.0370.9900.5050.057
20 km Miejsce Open0.9760.9670.9920.9791.0000.9210.9520.9440.1210.5220.0180.9990.4950.3280.653-0.0250.9850.4220.315
20 km Tempo0.8500.9030.8840.9470.9211.0000.8140.8360.1070.5040.0000.9260.3820.2750.630-0.0480.9580.6880.102
5 km Miejsce Open0.9910.9510.9750.9120.9520.8141.0000.9920.1260.4750.0200.9470.5120.3460.596-0.0190.9310.1950.314
5 km Tempo0.9830.9650.9670.9230.9440.8360.9921.0000.1350.4680.0310.9390.4720.3150.589-0.0260.9440.2270.093
Kategoria wiekowa0.1280.1430.1260.1280.1210.1070.1260.1351.0000.2000.013-0.229-0.1451.0000.397-0.605-0.230-0.0310.026
Kategoria wiekowa Miejsce0.4900.4830.5050.5050.5220.5040.4750.4680.2001.0000.0180.5240.2510.3830.7930.0980.5130.2580.190
Kraj0.0250.0210.0210.0220.0180.0000.0200.0310.0130.0181.000-0.0050.0060.0000.0010.029-0.0060.0150.033
Miejsce0.9720.9640.9890.9780.9990.9260.9470.939-0.2290.524-0.0051.0000.4920.3240.656-0.0270.9860.4340.315
Numer startowy0.5080.4460.5020.4350.4950.3820.5120.472-0.1450.2510.0060.4921.0000.1570.3000.0460.4420.0790.377
Płeć0.3500.3360.3440.3260.3280.2750.3460.3151.0000.3830.0000.3240.1571.0000.457-0.077-0.331-0.0760.015
Płeć Miejsce0.6140.6070.6320.6340.6530.6300.5960.5890.3970.7930.0010.6560.3000.4571.000-0.0780.6440.3320.261
Rocznik-0.023-0.038-0.024-0.037-0.025-0.048-0.019-0.026-0.6050.0980.029-0.0270.046-0.077-0.0781.000-0.037-0.0690.000
Tempo0.9570.9790.9750.9900.9850.9580.9310.944-0.2300.513-0.0060.9860.442-0.3310.644-0.0371.0000.4900.043
Tempo Stabilność0.2630.3760.3380.5050.4220.6880.1950.227-0.0310.2580.0150.4340.079-0.0760.332-0.0690.4901.0000.236
rok0.3140.0340.3150.0570.3150.1020.3140.0930.0260.1900.0330.3150.3770.0150.2610.0000.0430.2361.000

Missing values

2025-05-05T20:45:53.692435image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-05T20:45:53.868369image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-05T20:45:54.088761image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MiejsceNumer startowyImięNazwiskoMiastoKrajDrużynaPłećPłeć MiejsceKategoria wiekowaKategoria wiekowa MiejsceRocznik5 km Czas5 km Miejsce Open5 km Tempo10 km Czas10 km Miejsce Open10 km Tempo15 km Czas15 km Miejsce Open15 km Tempo20 km Czas20 km Miejsce Open20 km TempoTempo StabilnośćCzasTemporok
01.01787TOMASZGRYCKONaNPOLUKS BLIZA WŁADYSŁAWOWOM1.0M301.01992.000:14:371.02.92333300:29:151.02.92666700:44:471.03.10666701:01:431.03.3866670.03140001:04:593.0805092023
12.03ARKADIUSZGARDZIELEWSKIWROCŁAWPOLARKADIUSZGARDZIELEWSKI.PLM2.0M302.01986.000:14:482.02.96000000:29:432.02.98333300:45:262.03.14333301:03:082.03.5400000.03800001:06:233.1468752023
23.03832KRZYSZTOFHADASPOZNAŃPOLNaNM3.0M201.01996.000:15:464.03.15333300:31:233.03.12333300:47:343.03.23666701:05:093.03.5166670.02406701:08:243.2424752023
34.0416DAMIANDYDUCHKĘPNOPOLAZS POLITECHNIKA OPOLSKAM4.0M303.01988.000:16:116.03.23666700:32:105.03.19666700:48:495.03.33000001:06:544.03.6166670.02546701:10:163.3309632023
45.08476KAMILMAŃKOWSKIMIRKÓWPOLPARKRUN WROCŁAWM5.0M202.01995.000:16:127.03.24000000:32:357.03.27666700:49:317.03.38666701:07:275.03.5866670.02300001:10:273.3396542023
56.02551ADAMPUTYRAWROCŁAWPOLNaNM6.0M401.01983.000:16:095.03.23000000:32:306.03.27000000:49:316.03.40333301:07:286.03.5900000.02426701:10:343.3451842023
67.01288MICHAŁWÓJCIKKROŚNICEPOLWOSIEK TEAM KS AZS AWF WROCŁAWM7.0M203.01999.000:15:373.03.12333300:31:254.03.16000000:48:184.03.37666701:07:387.03.8666670.04893301:11:183.3799482023
78.07837PATRYKCHRZANOWSKIBIELAWAPOLPCH SPORT COMPLEXM8.0M304.01989.000:16:308.03.30000000:32:558.03.28333300:49:578.03.40666701:08:188.03.6700000.02466701:11:423.3989102023
89.05657CYPRIANGRZELKAPOGRZEBIEŃPOLGKS PIAST GLIWICEM9.0M204.02001.000:17:1014.03.43333300:34:2316.03.44333300:52:1112.03.56000001:11:0310.03.7733330.02273301:14:163.5205812023
910.05927ADAMKONIECZNYZANIEMYSLPOLNaNM10.0M305.01992.000:16:5311.03.37666700:33:5511.03.40666700:51:3910.03.54666701:10:509.03.8366670.03040001:14:223.5253222023
MiejsceNumer startowyImięNazwiskoMiastoKrajDrużynaPłećPłeć MiejsceKategoria wiekowaKategoria wiekowa MiejsceRocznik5 km Czas5 km Miejsce Open5 km Tempo10 km Czas10 km Miejsce Open10 km Tempo15 km Czas15 km Miejsce Open15 km Tempo20 km Czas20 km Miejsce Open20 km TempoTempo StabilnośćCzasTemporok
21947NaN23496RAFAŁŻULIŃSKINaNNaNNaNMNaNM40NaN1980.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024
21948NaN78027SEBASTIANŻURAWSKINaNNaNNaNMNaNM30NaN1988.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024
21949NaN21184KAMILŻUREKNaNNaN#2268MNaNM30NaN1985.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024
21950NaN25626JAGODAŻUREKNaNNaNBrakKNaNK20NaN1995.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024
21951NaN77701JANŻUREKNaNNaNBiegający EmerytMNaNM60NaN1960.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024
21952NaN6445ANNAŻUROWSKANaNNaNNaNKNaNK40NaN1982.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN2024
21953NaN23495JUSTYNAŻYGADŁONaNNaNNaNKNaNK20NaN1998.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024
21954NaN9323DAWIDŻYTKOWSKINaNNaNNaNMNaNM20NaN1995.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN2024
21955NaN27386DOMINIKAĆWIERTNIANaNNaNNaNKNaNK30NaN1991.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024
21956NaN80025KRZYSZTOFĆWIĘKNaNNaNPkoMNaNM30NaN1988.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024